ArticleCrystal growth & design2025
Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials.
Article in Crystal growth & design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Exploring celecoxib polymorph landscape using AIMNet2 machine learning interatomic potential.Chemical science · 2026Article
- From polymorphs to cocrystals and salts: successfully predicting axitinib's challenging crystal forms.Chemical science · 2025Article
- Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press.Journal of chemical theory and computation · 2025Article
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Authors and funding
10 authors.
Funding
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Abstract
Identifying thermodynamically stable crystal structures remains a key challenge in materials chemistry. Computational crystal structure prediction (CSP) workflows typically rank candidate structures by lattice energy to assess relative stability. Approaches using self-consistent first-principles calculations become prohibitively expensive, especially when millions of energy evaluations are required for complex molecular systems with many atoms per unit cell. Here, we provide a detailed analysis of our methodology and results from the seventh blind test of crystal structure prediction organized by the Cambridge Crystallographic Data Centre (CCDC). We present an approach that significantly accelerates CSP by training target-specific machine-learned interatomic potentials (MLIPs). AIMNet2 MLIPs are trained on density functional theory (DFT) calculations of molecular clusters, herein referred to as
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Registered trials
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